



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
	from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def  lowerCAmelCase_	(lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		List[str]=None		,						lowerCAmelCase__:		Any=None		,						lowerCAmelCase__:		Union[str, Any]=None		,						lowerCAmelCase__:		str=None		,						lowerCAmelCase__:		List[str]=None		,						):





			"""simple docstring"""


			if attention_mask is None:
						UpperCAmelCase_:		Optional[Any]							      =     input_ids.ne(config.pad_token_id  )
			if decoder_attention_mask is None:
						UpperCAmelCase_:		Dict							      =     decoder_input_ids.ne(config.pad_token_id  )
			if head_mask is None:
						UpperCAmelCase_:		Optional[Any]							      =     torch.ones(config.encoder_layers		,						config.encoder_attention_heads		,						device=lowerCAmelCase__  )
			if decoder_head_mask is None:
						UpperCAmelCase_:		Optional[Any]							      =     torch.ones(config.decoder_layers		,						config.decoder_attention_heads		,						device=lowerCAmelCase__  )
			if cross_attn_head_mask is None:
						UpperCAmelCase_:		Dict							      =     torch.ones(config.decoder_layers		,						config.decoder_attention_heads		,						device=lowerCAmelCase__  )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_="relu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=20,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0,   )       ->   Optional[int]:
						UpperCAmelCase_:		int							      =     parent
						UpperCAmelCase_:		Any							      =     batch_size
						UpperCAmelCase_:		Optional[int]							      =     seq_length
						UpperCAmelCase_:		Optional[int]							      =     is_training
						UpperCAmelCase_:		str							      =     use_labels
						UpperCAmelCase_:		Union[str, Any]							      =     vocab_size
						UpperCAmelCase_:		int							      =     hidden_size
						UpperCAmelCase_:		List[str]							      =     num_hidden_layers
						UpperCAmelCase_:		Optional[int]							      =     num_attention_heads
						UpperCAmelCase_:		Union[str, Any]							      =     intermediate_size
						UpperCAmelCase_:		Tuple							      =     hidden_act
						UpperCAmelCase_:		List[Any]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Dict							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Tuple							      =     encoder_layerdrop
						UpperCAmelCase_:		Any							      =     decoder_layerdrop
						UpperCAmelCase_:		List[str]							      =     max_position_embeddings
						UpperCAmelCase_:		str							      =     eos_token_id
						UpperCAmelCase_:		Optional[int]							      =     pad_token_id
						UpperCAmelCase_:		List[Any]							      =     bos_token_id

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Union[str, Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)
						UpperCAmelCase_:		List[str]							      =     self.eos_token_id  # Eos Token
						UpperCAmelCase_:		List[Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for M2M100 the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						UpperCAmelCase_:		Optional[int]							      =     input_ids.clamp(self.pad_token_id + 1					)
						UpperCAmelCase_:		Union[str, Any]							      =     decoder_input_ids.clamp(self.pad_token_id + 1					)

						UpperCAmelCase_:		List[str]							      =     self.get_config()
						UpperCAmelCase_:		Tuple							      =     prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						return config, inputs_dict

			def      __snake_case       (self					)       ->   Any:
						return MaMaaaConfig(
						    vocab_size=self.vocab_size,   d_model=self.hidden_size,   encoder_layers=self.num_hidden_layers,   decoder_layers=self.num_hidden_layers,   encoder_attention_heads=self.num_attention_heads,   decoder_attention_heads=self.num_attention_heads,   encoder_ffn_dim=self.intermediate_size,   decoder_ffn_dim=self.intermediate_size,   dropout=self.hidden_dropout_prob,   attention_dropout=self.attention_probs_dropout_prob,   encoder_layerdrop=self.encoder_layerdrop,   decoder_layerdrop=self.decoder_layerdrop,   max_position_embeddings=self.max_position_embeddings,   eos_token_id=self.eos_token_id,   bos_token_id=self.bos_token_id,   pad_token_id=self.pad_token_id,   )

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.prepare_config_and_inputs()
						return config, inputs_dict

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     MaMaaaModel(config=SCREAMING_SNAKE_CASE_					).get_decoder().to(SCREAMING_SNAKE_CASE_					).eval()
						UpperCAmelCase_:		Union[str, Any]							      =     inputs_dict["""input_ids"""]
						UpperCAmelCase_:		Dict							      =     inputs_dict["""attention_mask"""]
						UpperCAmelCase_:		Dict							      =     inputs_dict["""head_mask"""]

						# first forward pass
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   head_mask=SCREAMING_SNAKE_CASE_,   use_cache=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     outputs.to_tuple()

						# create hypothetical multiple next token and extent to next_input_ids
						UpperCAmelCase_:		Optional[Any]							      =     ids_tensor((self.batch_size, 3),   config.vocab_size					)
						UpperCAmelCase_:		Tuple							      =     ids_tensor((self.batch_size, 3),   2					)

						# append to next input_ids and
						UpperCAmelCase_:		str							      =     torch.cat([input_ids, next_tokens],   dim=-1					)
						UpperCAmelCase_:		Optional[int]							      =     torch.cat([attention_mask, next_attn_mask],   dim=-1					)

						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_					)["""last_hidden_state"""]
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   past_key_values=SCREAMING_SNAKE_CASE_					)[
						    """last_hidden_state"""
						]

						# select random slice
						UpperCAmelCase_:		Union[str, Any]							      =     ids_tensor((1,),   output_from_past.shape[-1]					).item()
						UpperCAmelCase_:		int							      =     output_from_no_past[:, -3:, random_slice_idx].detach()
						UpperCAmelCase_:		Optional[int]							      =     output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   atol=1E-2					)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     MaMaaaModel(config=SCREAMING_SNAKE_CASE_					).to(SCREAMING_SNAKE_CASE_					).eval()
						UpperCAmelCase_:		Dict							      =     model(**SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     outputs.encoder_last_hidden_state
						UpperCAmelCase_:		Tuple							      =     outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
									UpperCAmelCase_:		str							      =     model.get_encoder()
									encoder.save_pretrained(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Optional[int]							      =     MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_					).to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     encoder(inputs_dict["""input_ids"""],   attention_mask=inputs_dict["""attention_mask"""]					)[
						    0
						]

						self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3					)

						with tempfile.TemporaryDirectory() as tmpdirname:
									UpperCAmelCase_:		str							      =     model.get_decoder()
									decoder.save_pretrained(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Tuple							      =     MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_					).to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     decoder(
						    input_ids=inputs_dict["""decoder_input_ids"""],   attention_mask=inputs_dict["""decoder_attention_mask"""],   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   encoder_attention_mask=inputs_dict["""attention_mask"""],   )[0]

						self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3					)







@require_torch
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        MaMaaaModel,
			        MaMaaaForConditionalGeneration,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
			A      		=	(
			    {
			        '''conversational''': MaMaaaForConditionalGeneration,
			        '''feature-extraction''': MaMaaaModel,
			        '''summarization''': MaMaaaForConditionalGeneration,
			        '''text2text-generation''': MaMaaaForConditionalGeneration,
			        '''translation''': MaMaaaForConditionalGeneration,
			    }
			    if is_torch_available()
			    else {}
			)
			A      		=	True
			A      		=	True
			A      		=	False
			A      		=	False

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						if pipeline_test_casse_name == "TranslationPipelineTests":
									# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
									# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
									return True

						return False

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		int							      =     MaMaaaModelTester(self					)
						UpperCAmelCase_:		Any							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									UpperCAmelCase_:		str							      =     model_class(SCREAMING_SNAKE_CASE_					)

									with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_,   output_loading_info=SCREAMING_SNAKE_CASE_					)
									self.assertEqual(info["""missing_keys"""],   []					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
									UpperCAmelCase_:		Optional[int]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									model.to(SCREAMING_SNAKE_CASE_					)
									model.eval()

									UpperCAmelCase_:		Tuple							      =     copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)

									if not self.is_encoder_decoder:
												UpperCAmelCase_:		int							      =     inputs["""input_ids"""]
												del inputs["input_ids"]
									else:
												UpperCAmelCase_:		Dict							      =     inputs["""input_ids"""]
												UpperCAmelCase_:		Union[str, Any]							      =     inputs.get("""decoder_input_ids""",   SCREAMING_SNAKE_CASE_					)
												del inputs["input_ids"]
												inputs.pop("""decoder_input_ids""",   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Optional[Any]							      =     model.get_input_embeddings()
									if not self.is_encoder_decoder:
												UpperCAmelCase_:		str							      =     wte(SCREAMING_SNAKE_CASE_					)
									else:
												UpperCAmelCase_:		int							      =     wte(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		List[str]							      =     wte(SCREAMING_SNAKE_CASE_					)

									with torch.no_grad():
												model(**SCREAMING_SNAKE_CASE_					)[0]

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs()
						UpperCAmelCase_:		Union[str, Any]							      =     input_dict["""input_ids"""]
						UpperCAmelCase_:		Tuple							      =     input_ids.ne(1					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_					).eval().to(SCREAMING_SNAKE_CASE_					)
						if torch_device == "cuda":
									model.half()
						model.generate(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_					)
						model.generate(num_beams=4,   do_sample=SCREAMING_SNAKE_CASE_,   early_stopping=SCREAMING_SNAKE_CASE_,   num_return_sequences=3					)







def  lowerCAmelCase_	(lowerCAmelCase__:		List[str]  ):





			"""simple docstring"""


			return torch.tensor(lowerCAmelCase__		,						dtype=torch.long		,						device=lowerCAmelCase__  )


a      :  Any			     =			1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 				_a				(   unittest.TestCase					):

			@cached_property
			def      __snake_case       (self					)       ->   List[Any]:
						return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Dict							      =     MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
						UpperCAmelCase_:		int							      =     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
						UpperCAmelCase_:		List[str]							      =     prepare_mam_aaa_inputs_dict(model.config,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						with torch.no_grad():
									UpperCAmelCase_:		Tuple							      =     model(**SCREAMING_SNAKE_CASE_					)[0]
						UpperCAmelCase_:		int							      =     torch.Size((1, 11, 1024)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)
						# change to expected output here
						UpperCAmelCase_:		Tuple							      =     torch.tensor(
						    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]],   device=SCREAMING_SNAKE_CASE_					)
						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=SCREAMING_SNAKE_CASE_					)					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		int							      =     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(SCREAMING_SNAKE_CASE_					)

						# change to intended input
						UpperCAmelCase_:		List[str]							      =     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
						UpperCAmelCase_:		List[Any]							      =     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
						UpperCAmelCase_:		Optional[int]							      =     prepare_mam_aaa_inputs_dict(model.config,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						with torch.no_grad():
									UpperCAmelCase_:		List[str]							      =     model(**SCREAMING_SNAKE_CASE_					)[0]
						UpperCAmelCase_:		Union[str, Any]							      =     torch.Size((1, 11, model.config.vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)
						# change to expected output here
						UpperCAmelCase_:		List[Any]							      =     torch.tensor(
						    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]],   device=SCREAMING_SNAKE_CASE_					)
						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=SCREAMING_SNAKE_CASE_					)					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		List[str]							      =     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""",   src_lang="""fr""",   tgt_lang="""en"""					)

						UpperCAmelCase_:		List[Any]							      =     [
						    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
						    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
						    """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
						    """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
						    """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
						]

						# The below article tests that we don't add any hypotheses outside of the top n_beams
						UpperCAmelCase_:		Dict							      =     tokenizer(SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					)

						UpperCAmelCase_:		List[Any]							      =     model.generate(
						    input_ids=dct["""input_ids"""].to(SCREAMING_SNAKE_CASE_					),   attention_mask=dct["""attention_mask"""].to(SCREAMING_SNAKE_CASE_					),   num_beams=5,   forced_bos_token_id=tokenizer.get_lang_id("""en"""					),   )

						UpperCAmelCase_:		Dict							      =     [
						    """The NSA case highlights the total absence of intelligence debate""",
						    """I think there are two levels of response from the French government.""",
						    """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
						    """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
						    """ communications in France.""",
						]

						UpperCAmelCase_:		List[str]							      =     tokenizer.batch_decode(
						    hypotheses_batch.tolist(),   clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,   skip_special_tokens=SCREAMING_SNAKE_CASE_					)
						assert generated == expected_en











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






